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Apollo LiDAR obstacle perception module

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Program Information

Name: Apollo LiDAR obstacle perception module
Domain: Machine learning
Functionality: Interpret the point cloud data taken from the LiDAR
Input: A: three-dimensional point cloud data B: a small number of additional LiDAR data points randomly scattered in regions outside the ROI
Output: O: the obstacles detected

Reference


    Metamorphic testing of driverless cars.
	https://dx.doi.org/10.1145/3241979


MR Information

MR1------

Description:
Property: Let $A$ and $A′$ be two frames of three-dimensional point cloud data that are identical except that $A′$ includes a small number of additional LiDAR data points randomly scattered in regions outside the ROI. Also let $O$ and $O′$ be the sets of obstacles identified by LOP for $A$ and $A′$, respectively (LOP identifies only obstacles within the ROI). The following relation must then hold:$O ⊆ O′$.
Source input: $A_{s}$
Source output: $O_{s}$
Follow-up input: $A_{f}$, B
Follow-up output: O'
Input relation: $A_{f} \equiv A_{s}\ AND\ B$
Output relation: $O_{s}\subseteq O_{f}$
Pattern: symmetry, add noise
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